Week 7 Practice Questions_windows-env_py310-kernel.ipynb

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      Week 7 Practice Questions_windows-env_py310-kernel

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      [19]:
      # Attempt with the week 7 generated example data
      # Generate time-series data
      import pandas as pd
      import matplotlib.pyplot as plt
      data = {
      'Date': pd.date_range(start='2024-01-01', periods=10, freq='D'),
      'Sales': [200, 250, 270, 300, 400, 410, 380, 350, 370, 420]
      }
      df = pd.DataFrame(data)

      # Plotting the sales
      plt.figure(figsize=(10, 5))
      plt.plot(df['Date'], df['Sales'], label='Sales')
      plt.title('Sales Over Time')
      plt.xlabel('Date')
      plt.ylabel('Sale')
      plt.legend()
      plt.xticks(rotation=45)
      plt.tight_layout()
      plt.show()
      [9]:
      # Day 1: Visualizing Time-Series Data
      # Task 1: Plotting Stock Prices over Time
      # Generate time-series data
      # Sample dataset (replace with actual data)
      import pandas as pd
      import matplotlib.pyplot as plt

      # Generate time-series data
      data = {
      'Date': pd.date_range(start='2023-01-01', periods=365, freq='D'),
      'Stock Price': [100 + x + (x % 30) for x in range(365)]
      }
      df = pd.DataFrame(data)

      # Simulate missing data
      df.loc[100:120, 'Stock Price'] = None # 'Stock Price' column had missing values

      # Fill missing values by forward fill
      df['Stock Price'].ffill(inplace=True)

      # Plotting the stock prices
      plt.figure(figsize=(10, 6))
      plt.plot(df['Date'], df['Stock Price'], label='Stock Price')
      plt.title('Stock Prices Over Time')
      plt.xlabel('Date')
      plt.ylabel('Price')
      plt.legend()
      plt.xticks(rotation=45)
      plt.tight_layout()
      plt.show()

      [20]:
      print(df.columns)

      Index(['Date', 'Sales'], dtype='object')
      [25]:
      # Task 2: Visualizing Seasonality and Trends
      # Solution 1: Use period=30 for Monthly Seasonality (One Year of Data)
      import pandas as pd
      import matplotlib.pyplot as plt
      from statsmodels.tsa.seasonal import seasonal_decompose

      # Generate time-series data
      data = {
      'Date': pd.date_range(start='2023-01-01', periods=365, freq='D'),
      'Stock Price': [100 + x + (x % 30) for x in range(365)]
      }
      df = pd.DataFrame(data)

      # Ensure the 'Stock Price' column is numeric
      df['Stock Price'] = pd.to_numeric(df['Stock Price'], errors='coerce')

      # Perform seasonal decomposition with period=30 (monthly seasonality)
      result = seasonal_decompose(df['Stock Price'], model='multiplicative', period=30)

      # Plot the decomposition
      result.plot()
      plt.show()


      [26]:
      # Task 2: Visualizing Seasonality and Trends
      # Solution 2: Extend the Data to 2 Years (730 Observations) for Yearly Seasonality
      # Generate two years of time-series data (730 observations)
      data = {
      'Date': pd.date_range(start='2023-01-01', periods=730, freq='D'), # Two years of daily data
      'Stock Price': [100 + x + (x % 30) for x in range(730)] # Extended to two years
      }
      df = pd.DataFrame(data)

      # Ensure the 'Stock Price' column is numeric
      df['Stock Price'] = pd.to_numeric(df['Stock Price'], errors='coerce')

      # Perform seasonal decomposition with period=365 (two years of data)
      result = seasonal_decompose(df['Stock Price'], model='multiplicative', period=365)

      # Plot the decomposition
      result.plot()
      plt.show()

      [27]:
      # Task 3: Visualizing Daily Temperature Trends
      from statsmodels.tsa.seasonal import seasonal_decompose

      # Assuming df['Stock Price'] is already in the correct format
      result = seasonal_decompose(df['Stock Price'], model='multiplicative', period=365)

      # Plot the decomposition
      result.plot()
      plt.show()

      # Explanation of components:
      # Trend: The underlying trend over time.
      # Seasonality: Patterns that repeat at regular intervals (e.g., yearly).
      # Residual: What's left after removing trend and seasonality.

      [29]:
      # Task 4: Handling Irregular Time Intervals
      # Sample dataset with irregular intervals
      df_irregular = pd.DataFrame({
      'Date': ['2023-01-01', '2023-01-03', '2023-01-05', '2023-01-07'],
      'Stock Price': [100, 105, 110, 115]
      })
      df_irregular['Date'] = pd.to_datetime(df_irregular['Date'])
      df_irregular.set_index('Date', inplace=True)

      # Interpolate missing dates
      df_irregular_resampled = df_irregular.resample('D').interpolate(method='linear')

      # Plotting the result
      plt.figure(figsize=(8, 6))
      plt.plot(df_irregular_resampled.index, df_irregular_resampled['Stock Price'], label='Interpolated Stock Prices')
      plt.title('Stock Prices with Interpolated Missing Data')
      plt.xlabel('Date')
      plt.ylabel('Price')
      plt.xticks(rotation=45)
      plt.tight_layout()
      plt.show()

      [3]:
      !pip install folium
      Defaulting to user installation because normal site-packages is not writeable
Looking in links: /usr/share/pip-wheels
Collecting folium
  Downloading folium-0.18.0-py2.py3-none-any.whl.metadata (3.8 kB)
Collecting branca>=0.6.0 (from folium)
  Downloading branca-0.8.0-py3-none-any.whl.metadata (1.5 kB)
Requirement already satisfied: jinja2>=2.9 in /opt/conda/envs/anaconda-2024.02-py310/lib/python3.10/site-packages (from folium) (3.1.3)
Requirement already satisfied: numpy in /opt/conda/envs/anaconda-2024.02-py310/lib/python3.10/site-packages (from folium) (1.26.4)
Requirement already satisfied: requests in /opt/conda/envs/anaconda-2024.02-py310/lib/python3.10/site-packages (from folium) (2.31.0)
Requirement already satisfied: xyzservices in /opt/conda/envs/anaconda-2024.02-py310/lib/python3.10/site-packages (from folium) (2022.9.0)
Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/anaconda-2024.02-py310/lib/python3.10/site-packages (from jinja2>=2.9->folium) (2.1.3)
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Requirement already satisfied: idna<4,>=2.5 in /opt/conda/envs/anaconda-2024.02-py310/lib/python3.10/site-packages (from requests->folium) (3.4)
Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/envs/anaconda-2024.02-py310/lib/python3.10/site-packages (from requests->folium) (2.0.7)
Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/envs/anaconda-2024.02-py310/lib/python3.10/site-packages (from requests->folium) (2024.2.2)
Downloading folium-0.18.0-py2.py3-none-any.whl (108 kB)
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Downloading branca-0.8.0-py3-none-any.whl (25 kB)
Installing collected packages: branca, folium
Successfully installed branca-0.8.0 folium-0.18.0
      [4]:
      !pip show folium
      Name: folium
Version: 0.18.0
Summary: Make beautiful maps with Leaflet.js & Python
Home-page: https://github.com/python-visualization/folium
Author: Rob Story
Author-email: wrobstory@gmail.com
License: MIT
Location: /home/6f348dcd-6f88-48fa-ab3c-868ada9b6b8b/.local/lib/python3.10/site-packages
Requires: branca, jinja2, numpy, requests, xyzservices
Required-by:
      [6]:
      import sys
      print(sys.executable)
      /opt/conda/envs/anaconda-2024.02-py310/bin/python
      [7]:
      !which python
      /opt/conda/envs/anaconda-2024.02-py310/bin/python
      [8]:
      !pip install --upgrade --user folium
      Looking in links: /usr/share/pip-wheels
Requirement already satisfied: folium in ./.local/lib/python3.10/site-packages (0.18.0)
Requirement already satisfied: branca>=0.6.0 in ./.local/lib/python3.10/site-packages (from folium) (0.8.0)
Requirement already satisfied: jinja2>=2.9 in /opt/conda/envs/anaconda-2024.02-py310/lib/python3.10/site-packages (from folium) (3.1.3)
Requirement already satisfied: numpy in /opt/conda/envs/anaconda-2024.02-py310/lib/python3.10/site-packages (from folium) (1.26.4)
Requirement already satisfied: requests in /opt/conda/envs/anaconda-2024.02-py310/lib/python3.10/site-packages (from folium) (2.31.0)
Requirement already satisfied: xyzservices in /opt/conda/envs/anaconda-2024.02-py310/lib/python3.10/site-packages (from folium) (2022.9.0)
Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/anaconda-2024.02-py310/lib/python3.10/site-packages (from jinja2>=2.9->folium) (2.1.3)
Requirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/envs/anaconda-2024.02-py310/lib/python3.10/site-packages (from requests->folium) (2.0.4)
Requirement already satisfied: idna<4,>=2.5 in /opt/conda/envs/anaconda-2024.02-py310/lib/python3.10/site-packages (from requests->folium) (3.4)
Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/envs/anaconda-2024.02-py310/lib/python3.10/site-packages (from requests->folium) (2.0.7)
Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/envs/anaconda-2024.02-py310/lib/python3.10/site-packages (from requests->folium) (2024.2.2)
      [1]:
      !pip install folium
      Defaulting to user installation because normal site-packages is not writeable
Looking in links: /usr/share/pip-wheels
Requirement already satisfied: folium in ./.local/lib/python3.10/site-packages (0.18.0)
Requirement already satisfied: branca>=0.6.0 in ./.local/lib/python3.10/site-packages (from folium) (0.8.0)
Requirement already satisfied: jinja2>=2.9 in /opt/conda/envs/anaconda-2024.02-py310/lib/python3.10/site-packages (from folium) (3.1.3)
Requirement already satisfied: numpy in /opt/conda/envs/anaconda-2024.02-py310/lib/python3.10/site-packages (from folium) (1.26.4)
Requirement already satisfied: requests in /opt/conda/envs/anaconda-2024.02-py310/lib/python3.10/site-packages (from folium) (2.31.0)
Requirement already satisfied: xyzservices in /opt/conda/envs/anaconda-2024.02-py310/lib/python3.10/site-packages (from folium) (2022.9.0)
Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/anaconda-2024.02-py310/lib/python3.10/site-packages (from jinja2>=2.9->folium) (2.1.3)
Requirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/envs/anaconda-2024.02-py310/lib/python3.10/site-packages (from requests->folium) (2.0.4)
Requirement already satisfied: idna<4,>=2.5 in /opt/conda/envs/anaconda-2024.02-py310/lib/python3.10/site-packages (from requests->folium) (3.4)
Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/envs/anaconda-2024.02-py310/lib/python3.10/site-packages (from requests->folium) (2.0.7)
Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/envs/anaconda-2024.02-py310/lib/python3.10/site-packages (from requests->folium) (2024.2.2)
      [2]:
      import folium
      print(folium.__version__)
      0.18.0
      [5]:
      import pandas as pd
      import folium

      # Sample dataset (replace with actual data)
      cities = {
      'City': ['New York', 'Los Angeles', 'Chicago', 'Houston', 'Phoenix'],
      'Latitude': [40.7128, 34.0522, 41.8781, 29.7604, 33.4484],
      'Longitude': [-74.0060, -118.2437, -87.6298, -95.3698, -112.0740],
      'Sales': [1000, 1500, 1200, 1300, 1100]
      }

      # Convert the dictionary to a DataFrame
      df_cities = pd.DataFrame(cities)

      # Create a map centered around the US
      m = folium.Map(location=[39.8283, -98.5795], zoom_start=4)

      # Add markers for each city on the map
      for _, city in df_cities.iterrows():
      folium.Marker(
      location=[city['Latitude'], city['Longitude']],
      popup=f"City: {city['City']}<br>Sales: {city['Sales']}",
      ).add_to(m)

      # Display the map
      m.save("sales_map.html")

      [6]:
      from IPython.display import IFrame

      # Display the saved HTML map in the notebook
      IFrame('sales_map.html', width=700, height=500)
      [6]:
      [1]:
      import folium
      import pandas as pd
      from IPython.display import IFrame # Import IFrame for displaying the map inline

      # Updated dataset with Africa included
      data = {
      'Country': ['USA', 'China', 'India', 'Brazil', 'Germany', 'UK', 'France', 'Italy', 'Spain', 'Australia', 'Nigeria', 'South Africa'],
      'Population': [331002651, 1439323776, 1380004385, 212559417, 83783942, 67886011, 65273511, 60244639, 46719142, 25499884, 206139589, 59308690],
      'Region': ['North America', 'Asia', 'Asia', 'South America', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Oceania', 'Africa', 'Africa'],
      'Latitude': [37.0902, 35.8617, 20.5937, -14.2350, 51.1657, 55.3781, 46.6034, 41.8719, 40.4637, -25.2744, 9.0820, -30.5595],
      'Longitude': [-95.7129, 104.1954, 78.9629, -51.9253, 10.4515, -3.4360, 2.2137, 12.5674, -3.7492, 133.7751, 8.6753, 22.9375]
      }

      # Convert to DataFrame
      df = pd.DataFrame(data)

      # Create a base map centered around a location with an appropriate zoom level
      world_map = folium.Map(location=[20, 0], zoom_start=2)

      # Add population circles to the map with tooltips
      for idx, row in df.iterrows():
      folium.CircleMarker(
      location=[row['Latitude'], row['Longitude']],
      radius=row['Population'] ** 0.5 / 1000, # Scale radius by population for better visualization
      color='blue',
      fill=True,
      fill_color='blue',
      fill_opacity=0.7,
      tooltip=f"{row['Country']}<br>Population: {row['Population']:,}" # Tooltip for hover effect
      ).add_to(world_map)

      # Save map as an HTML file
      world_map.save("population_map.html")

      # Display map inline in Jupyter
      IFrame("population_map.html", width=700, height=500)
      [1]:
      [8]:
      !pip install geopandas
      Defaulting to user installation because normal site-packages is not writeable
Looking in links: /usr/share/pip-wheels
Collecting geopandas
  Downloading geopandas-1.0.1-py3-none-any.whl.metadata (2.2 kB)
Requirement already satisfied: numpy>=1.22 in /opt/conda/envs/anaconda-2024.02-py310/lib/python3.10/site-packages (from geopandas) (1.26.4)
Collecting pyogrio>=0.7.2 (from geopandas)
  Downloading pyogrio-0.10.0-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (5.5 kB)
Requirement already satisfied: packaging in /opt/conda/envs/anaconda-2024.02-py310/lib/python3.10/site-packages (from geopandas) (23.2)
Requirement already satisfied: pandas>=1.4.0 in /opt/conda/envs/anaconda-2024.02-py310/lib/python3.10/site-packages (from geopandas) (2.1.4)
Collecting pyproj>=3.3.0 (from geopandas)
  Downloading pyproj-3.7.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (31 kB)
Collecting shapely>=2.0.0 (from geopandas)
  Downloading shapely-2.0.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (7.0 kB)
Requirement already satisfied: python-dateutil>=2.8.2 in /opt/conda/envs/anaconda-2024.02-py310/lib/python3.10/site-packages (from pandas>=1.4.0->geopandas) (2.8.2)
Requirement already satisfied: pytz>=2020.1 in /opt/conda/envs/anaconda-2024.02-py310/lib/python3.10/site-packages (from pandas>=1.4.0->geopandas) (2023.3.post1)
Requirement already satisfied: tzdata>=2022.1 in /opt/conda/envs/anaconda-2024.02-py310/lib/python3.10/site-packages (from pandas>=1.4.0->geopandas) (2023.3)
Requirement already satisfied: certifi in /opt/conda/envs/anaconda-2024.02-py310/lib/python3.10/site-packages (from pyogrio>=0.7.2->geopandas) (2024.2.2)
Requirement already satisfied: six>=1.5 in /opt/conda/envs/anaconda-2024.02-py310/lib/python3.10/site-packages (from python-dateutil>=2.8.2->pandas>=1.4.0->geopandas) (1.16.0)
Downloading geopandas-1.0.1-py3-none-any.whl (323 kB)
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Downloading pyogrio-0.10.0-cp310-cp310-manylinux_2_28_x86_64.whl (23.9 MB)
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Downloading pyproj-3.7.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (9.2 MB)
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Downloading shapely-2.0.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.5 MB)
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Installing collected packages: shapely, pyproj, pyogrio, geopandas
  WARNING: The script pyproj is installed in '/home/6f348dcd-6f88-48fa-ab3c-868ada9b6b8b/.local/bin' which is not on PATH.
  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.
Successfully installed geopandas-1.0.1 pyogrio-0.10.0 pyproj-3.7.0 shapely-2.0.6
      [9]:
      import geopandas as gpd
      print(gpd.__version__) # Print version to verify
      1.0.1
      [28]:
      import os
      print(os.path.abspath("C:/Users/HP USER/Downloads/Countries/ne_10m_admin_0_countries.shp"))

      /home/6f348dcd-6f88-48fa-ab3c-868ada9b6b8b/C:/Users/HP USER/Downloads/Countries/ne_10m_admin_0_countries.shp
      [30]:
      import os
      print(os.getcwd()) # This shows the current working directory

      /home/6f348dcd-6f88-48fa-ab3c-868ada9b6b8b
      [31]:
      # Assuming you uploaded the files to the correct path in the working directory
      shapefile_path = '/home/6f348dcd-6f88-48fa-ab3c-868ada9b6b8b/ne_10m_admin_0_countries.shp'
      world = gpd.read_file(shapefile_path)
      print(world.head())

              featurecla  scalerank  LABELRANK SOVEREIGNT SOV_A3  ADM0_DIF  LEVEL  \
0  Admin-0 country          0          2  Indonesia    IDN         0      2   
1  Admin-0 country          0          3   Malaysia    MYS         0      2   
2  Admin-0 country          0          2      Chile    CHL         0      2   
3  Admin-0 country          0          3    Bolivia    BOL         0      2   
4  Admin-0 country          0          2       Peru    PER         0      2   

                TYPE TLC      ADMIN  ... FCLASS_TR  FCLASS_ID FCLASS_PL  \
0  Sovereign country   1  Indonesia  ...      None       None      None   
1  Sovereign country   1   Malaysia  ...      None       None      None   
2  Sovereign country   1      Chile  ...      None       None      None   
3  Sovereign country   1    Bolivia  ...      None       None      None   
4  Sovereign country   1       Peru  ...      None       None      None   

  FCLASS_GR  FCLASS_IT FCLASS_NL FCLASS_SE  FCLASS_BD FCLASS_UA  \
0      None       None      None      None       None      None   
1      None       None      None      None       None      None   
2      None       None      None      None       None      None   
3      None       None      None      None       None      None   
4      None       None      None      None       None      None   

                                            geometry  
0  MULTIPOLYGON (((117.70361 4.16341, 117.70361 4...  
1  MULTIPOLYGON (((117.70361 4.16341, 117.69711 4...  
2  MULTIPOLYGON (((-69.51009 -17.50659, -69.50611...  
3  POLYGON ((-69.51009 -17.50659, -69.51009 -17.5...  
4  MULTIPOLYGON (((-69.51009 -17.50659, -69.63832...  

[5 rows x 169 columns]
      [39]:
      # Check the geometry types
      print(world.geometry.geom_type.value_counts())

      MultiPolygon    7
Polygon         2
Name: count, dtype: int64
      [40]:
      from shapely.geometry import Polygon, MultiPolygon

      # Apply buffer(0) to invalid geometries of type Polygon and MultiPolygon
      world['geometry'] = world['geometry'].apply(
      lambda geom: geom.buffer(0) if isinstance(geom, (Polygon, MultiPolygon)) and not geom.is_valid else geom
      )

      # Verify again if the geometries are valid
      print(world.is_valid)

      # Plot the world map after fixing geometries
      world.plot(figsize=(10, 10))
      plt.show()

      0       True
1       True
2       True
3       True
4       True
       ...  
253    False
254    False
255    False
256    False
257    False
Length: 258, dtype: bool
      [41]:
      # Find and inspect invalid geometries
      invalid_geometries = world[~world.is_valid]
      print(invalid_geometries)

      # Optional: Inspect the geometries in detail
      for idx, row in invalid_geometries.iterrows():
      print(f"Invalid geometry at index {idx}: {row['geometry']}")

                featurecla  scalerank  LABELRANK                    SOVEREIGNT  \
9    Admin-0 country          0          2                         China   
10   Admin-0 country          0          4                        Israel   
11   Admin-0 country          0          5                        Israel   
12   Admin-0 country          0          5                       Lebanon   
13   Admin-0 country          0          2                      Ethiopia   
..               ...        ...        ...                           ...   
253  Admin-0 country          0          4                         China   
254  Admin-0 country          6          5                     Australia   
255  Admin-0 country          6          8  Bajo Nuevo Bank (Petrel Is.)   
256  Admin-0 country          6          5               Serranilla Bank   
257  Admin-0 country          6          6              Scarborough Reef   

    SOV_A3  ADM0_DIF  LEVEL               TYPE TLC  \
9      CH1         1      2            Country   1   
10     IS1         1      2           Disputed   1   
11     IS1         1      2      Indeterminate   1   
12     LBN         0      2  Sovereign country   1   
13     ETH         0      2  Sovereign country   1   
..     ...       ...    ...                ...  ..   
253    CH1         1      2            Country   1   
254    AU1         1      2         Dependency   1   
255    BJN         0      2      Indeterminate   1   
256    SER         0      2      Indeterminate   1   
257    SCR         0      2      Indeterminate   1   

                            ADMIN  ...     FCLASS_TR     FCLASS_ID  \
9                           China  ...          None          None   
10                         Israel  ...          None          None   
11                      Palestine  ...          None          None   
12                        Lebanon  ...          None          None   
13                       Ethiopia  ...          None          None   
..                            ...  ...           ...           ...   
253                   Macao S.A.R  ...          None          None   
254   Ashmore and Cartier Islands  ...          None          None   
255  Bajo Nuevo Bank (Petrel Is.)  ...  Unrecognized  Unrecognized   
256               Serranilla Bank  ...  Unrecognized  Unrecognized   
257              Scarborough Reef  ...          None          None   

        FCLASS_PL     FCLASS_GR     FCLASS_IT     FCLASS_NL     FCLASS_SE  \
9            None          None          None          None          None   
10           None          None          None          None          None   
11           None          None          None          None          None   
12           None          None          None          None          None   
13           None          None          None          None          None   
..            ...           ...           ...           ...           ...   
253          None          None          None          None          None   
254          None          None          None          None          None   
255  Unrecognized  Unrecognized  Unrecognized  Unrecognized  Unrecognized   
256  Unrecognized  Unrecognized  Unrecognized  Unrecognized  Unrecognized   
257          None          None          None          None          None   

           FCLASS_BD     FCLASS_UA geometry  
9               None          None     None  
10      Unrecognized          None     None  
11   Admin-0 country          None     None  
12              None          None     None  
13              None          None     None  
..               ...           ...      ...  
253             None          None     None  
254             None          None     None  
255     Unrecognized  Unrecognized     None  
256     Unrecognized  Unrecognized     None  
257             None          None     None  

[249 rows x 169 columns]
Invalid geometry at index 9: None
Invalid geometry at index 10: None
Invalid geometry at index 11: None
Invalid geometry at index 12: None
Invalid geometry at index 13: None
Invalid geometry at index 14: None
Invalid geometry at index 15: None
Invalid geometry at index 16: None
Invalid geometry at index 17: None
Invalid geometry at index 18: None
Invalid geometry at index 19: None
Invalid geometry at index 20: None
Invalid geometry at index 21: None
Invalid geometry at index 22: None
Invalid geometry at index 23: None
Invalid geometry at index 24: None
Invalid geometry at index 25: None
Invalid geometry at index 26: None
Invalid geometry at index 27: None
Invalid geometry at index 28: None
Invalid geometry at index 29: None
Invalid geometry at index 30: None
Invalid geometry at index 31: None
Invalid geometry at index 32: None
Invalid geometry at index 33: None
Invalid geometry at index 34: None
Invalid geometry at index 35: None
Invalid geometry at index 36: None
Invalid geometry at index 37: None
Invalid geometry at index 38: None
Invalid geometry at index 39: None
Invalid geometry at index 40: None
Invalid geometry at index 41: None
Invalid geometry at index 42: None
Invalid geometry at index 43: None
Invalid geometry at index 44: None
Invalid geometry at index 45: None
Invalid geometry at index 46: None
Invalid geometry at index 47: None
Invalid geometry at index 48: None
Invalid geometry at index 49: None
Invalid geometry at index 50: None
Invalid geometry at index 51: None
Invalid geometry at index 52: None
Invalid geometry at index 53: None
Invalid geometry at index 54: None
Invalid geometry at index 55: None
Invalid geometry at index 56: None
Invalid geometry at index 57: None
Invalid geometry at index 58: None
Invalid geometry at index 59: None
Invalid geometry at index 60: None
Invalid geometry at index 61: None
Invalid geometry at index 62: None
Invalid geometry at index 63: None
Invalid geometry at index 64: None
Invalid geometry at index 65: None
Invalid geometry at index 66: None
Invalid geometry at index 67: None
Invalid geometry at index 68: None
Invalid geometry at index 69: None
Invalid geometry at index 70: None
Invalid geometry at index 71: None
Invalid geometry at index 72: None
Invalid geometry at index 73: None
Invalid geometry at index 74: None
Invalid geometry at index 75: None
Invalid geometry at index 76: None
Invalid geometry at index 77: None
Invalid geometry at index 78: None
Invalid geometry at index 79: None
Invalid geometry at index 80: None
Invalid geometry at index 81: None
Invalid geometry at index 82: None
Invalid geometry at index 83: None
Invalid geometry at index 84: None
Invalid geometry at index 85: None
Invalid geometry at index 86: None
Invalid geometry at index 87: None
Invalid geometry at index 88: None
Invalid geometry at index 89: None
Invalid geometry at index 90: None
Invalid geometry at index 91: None
Invalid geometry at index 92: None
Invalid geometry at index 93: None
Invalid geometry at index 94: None
Invalid geometry at index 95: None
Invalid geometry at index 96: None
Invalid geometry at index 97: None
Invalid geometry at index 98: None
Invalid geometry at index 99: None
Invalid geometry at index 100: None
Invalid geometry at index 101: None
Invalid geometry at index 102: None
Invalid geometry at index 103: None
Invalid geometry at index 104: None
Invalid geometry at index 105: None
Invalid geometry at index 106: None
Invalid geometry at index 107: None
Invalid geometry at index 108: None
Invalid geometry at index 109: None
Invalid geometry at index 110: None
Invalid geometry at index 111: None
Invalid geometry at index 112: None
Invalid geometry at index 113: None
Invalid geometry at index 114: None
Invalid geometry at index 115: None
Invalid geometry at index 116: None
Invalid geometry at index 117: None
Invalid geometry at index 118: None
Invalid geometry at index 119: None
Invalid geometry at index 120: None
Invalid geometry at index 121: None
Invalid geometry at index 122: None
Invalid geometry at index 123: None
Invalid geometry at index 124: None
Invalid geometry at index 125: None
Invalid geometry at index 126: None
Invalid geometry at index 127: None
Invalid geometry at index 128: None
Invalid geometry at index 129: None
Invalid geometry at index 130: None
Invalid geometry at index 131: None
Invalid geometry at index 132: None
Invalid geometry at index 133: None
Invalid geometry at index 134: None
Invalid geometry at index 135: None
Invalid geometry at index 136: None
Invalid geometry at index 137: None
Invalid geometry at index 138: None
Invalid geometry at index 139: None
Invalid geometry at index 140: None
Invalid geometry at index 141: None
Invalid geometry at index 142: None
Invalid geometry at index 143: None
Invalid geometry at index 144: None
Invalid geometry at index 145: None
Invalid geometry at index 146: None
Invalid geometry at index 147: None
Invalid geometry at index 148: None
Invalid geometry at index 149: None
Invalid geometry at index 150: None
Invalid geometry at index 151: None
Invalid geometry at index 152: None
Invalid geometry at index 153: None
Invalid geometry at index 154: None
Invalid geometry at index 155: None
Invalid geometry at index 156: None
Invalid geometry at index 157: None
Invalid geometry at index 158: None
Invalid geometry at index 159: None
Invalid geometry at index 160: None
Invalid geometry at index 161: None
Invalid geometry at index 162: None
Invalid geometry at index 163: None
Invalid geometry at index 164: None
Invalid geometry at index 165: None
Invalid geometry at index 166: None
Invalid geometry at index 167: None
Invalid geometry at index 168: None
Invalid geometry at index 169: None
Invalid geometry at index 170: None
Invalid geometry at index 171: None
Invalid geometry at index 172: None
Invalid geometry at index 173: None
Invalid geometry at index 174: None
Invalid geometry at index 175: None
Invalid geometry at index 176: None
Invalid geometry at index 177: None
Invalid geometry at index 178: None
Invalid geometry at index 179: None
Invalid geometry at index 180: None
Invalid geometry at index 181: None
Invalid geometry at index 182: None
Invalid geometry at index 183: None
Invalid geometry at index 184: None
Invalid geometry at index 185: None
Invalid geometry at index 186: None
Invalid geometry at index 187: None
Invalid geometry at index 188: None
Invalid geometry at index 189: None
Invalid geometry at index 190: None
Invalid geometry at index 191: None
Invalid geometry at index 192: None
Invalid geometry at index 193: None
Invalid geometry at index 194: None
Invalid geometry at index 195: None
Invalid geometry at index 196: None
Invalid geometry at index 197: None
Invalid geometry at index 198: None
Invalid geometry at index 199: None
Invalid geometry at index 200: None
Invalid geometry at index 201: None
Invalid geometry at index 202: None
Invalid geometry at index 203: None
Invalid geometry at index 204: None
Invalid geometry at index 205: None
Invalid geometry at index 206: None
Invalid geometry at index 207: None
Invalid geometry at index 208: None
Invalid geometry at index 209: None
Invalid geometry at index 210: None
Invalid geometry at index 211: None
Invalid geometry at index 212: None
Invalid geometry at index 213: None
Invalid geometry at index 214: None
Invalid geometry at index 215: None
Invalid geometry at index 216: None
Invalid geometry at index 217: None
Invalid geometry at index 218: None
Invalid geometry at index 219: None
Invalid geometry at index 220: None
Invalid geometry at index 221: None
Invalid geometry at index 222: None
Invalid geometry at index 223: None
Invalid geometry at index 224: None
Invalid geometry at index 225: None
Invalid geometry at index 226: None
Invalid geometry at index 227: None
Invalid geometry at index 228: None
Invalid geometry at index 229: None
Invalid geometry at index 230: None
Invalid geometry at index 231: None
Invalid geometry at index 232: None
Invalid geometry at index 233: None
Invalid geometry at index 234: None
Invalid geometry at index 235: None
Invalid geometry at index 236: None
Invalid geometry at index 237: None
Invalid geometry at index 238: None
Invalid geometry at index 239: None
Invalid geometry at index 240: None
Invalid geometry at index 241: None
Invalid geometry at index 242: None
Invalid geometry at index 243: None
Invalid geometry at index 244: None
Invalid geometry at index 245: None
Invalid geometry at index 246: None
Invalid geometry at index 247: None
Invalid geometry at index 248: None
Invalid geometry at index 249: None
Invalid geometry at index 250: None
Invalid geometry at index 251: None
Invalid geometry at index 252: None
Invalid geometry at index 253: None
Invalid geometry at index 254: None
Invalid geometry at index 255: None
Invalid geometry at index 256: None
Invalid geometry at index 257: None
      [42]:
      from shapely.geometry import Polygon

      # Replace None geometries with an empty Polygon
      world['geometry'] = world['geometry'].apply(lambda geom: Polygon() if geom is None else geom)

      # Verify all geometries are now valid
      print(world.is_valid)

      # Plot the map
      world.plot(figsize=(10, 10))
      plt.show()

      0      True
1      True
2      True
3      True
4      True
       ... 
253    True
254    True
255    True
256    True
257    True
Length: 258, dtype: bool
      [47]:
      # Remove invalid geometries from the dataset
      valid_world = world[world.is_valid]

      # Save the cleaned dataset
      valid_file_path = '/home/6f348dcd-6f88-48fa-ab3c-868ada9b6b8b/valid_ne_10m_admin_0_countries.shp'
      valid_world.to_file(valid_file_path)

      print(f"Cleaned shapefile saved to: {valid_file_path}")
      /home/6f348dcd-6f88-48fa-ab3c-868ada9b6b8b/.local/lib/python3.10/site-packages/pyogrio/geopandas.py:662: UserWarning: 'crs' was not provided.  The output dataset will not have projection information defined and may not be usable in other systems.
  write(
/home/6f348dcd-6f88-48fa-ab3c-868ada9b6b8b/.local/lib/python3.10/site-packages/pyogrio/raw.py:723: RuntimeWarning: Value 270625568 of field POP_EST of feature 0 not successfully written. Possibly due to too larger number with respect to field width
  ogr_write(
/home/6f348dcd-6f88-48fa-ab3c-868ada9b6b8b/.local/lib/python3.10/site-packages/pyogrio/raw.py:723: RuntimeWarning: Value 1366417754 of field POP_EST of feature 8 not successfully written. Possibly due to too larger number with respect to field width
  ogr_write(
/home/6f348dcd-6f88-48fa-ab3c-868ada9b6b8b/.local/lib/python3.10/site-packages/pyogrio/raw.py:723: RuntimeWarning: Value 1397715000 of field POP_EST of feature 9 not successfully written. Possibly due to too larger number with respect to field width
  ogr_write(
/home/6f348dcd-6f88-48fa-ab3c-868ada9b6b8b/.local/lib/python3.10/site-packages/pyogrio/raw.py:723: RuntimeWarning: Value 112078730 of field POP_EST of feature 13 not successfully written. Possibly due to too larger number with respect to field width
  ogr_write(
/home/6f348dcd-6f88-48fa-ab3c-868ada9b6b8b/.local/lib/python3.10/site-packages/pyogrio/raw.py:723: RuntimeWarning: Value 211049527 of field POP_EST of feature 44 not successfully written. Possibly due to too larger number with respect to field width
  ogr_write(
/home/6f348dcd-6f88-48fa-ab3c-868ada9b6b8b/.local/lib/python3.10/site-packages/pyogrio/raw.py:723: RuntimeWarning: Value 144373535 of field POP_EST of feature 47 not successfully written. Possibly due to too larger number with respect to field width
  ogr_write(
/home/6f348dcd-6f88-48fa-ab3c-868ada9b6b8b/.local/lib/python3.10/site-packages/pyogrio/raw.py:723: RuntimeWarning: Value 200963599 of field POP_EST of feature 99 not successfully written. Possibly due to too larger number with respect to field width
  ogr_write(
/home/6f348dcd-6f88-48fa-ab3c-868ada9b6b8b/.local/lib/python3.10/site-packages/pyogrio/raw.py:723: RuntimeWarning: Value 216565318 of field POP_EST of feature 108 not successfully written. Possibly due to too larger number with respect to field width
  ogr_write(
/home/6f348dcd-6f88-48fa-ab3c-868ada9b6b8b/.local/lib/python3.10/site-packages/pyogrio/raw.py:723: RuntimeWarning: Value 163046161 of field POP_EST of feature 127 not successfully written. Possibly due to too larger number with respect to field width
  ogr_write(
/home/6f348dcd-6f88-48fa-ab3c-868ada9b6b8b/.local/lib/python3.10/site-packages/pyogrio/raw.py:723: RuntimeWarning: Value 328239523 of field POP_EST of feature 154 not successfully written. Possibly due to too larger number with respect to field width
  ogr_write(
/home/6f348dcd-6f88-48fa-ab3c-868ada9b6b8b/.local/lib/python3.10/site-packages/pyogrio/raw.py:723: RuntimeWarning: Value 127575529 of field POP_EST of feature 156 not successfully written. Possibly due to too larger number with respect to field width
  ogr_write(
/home/6f348dcd-6f88-48fa-ab3c-868ada9b6b8b/.local/lib/python3.10/site-packages/pyogrio/raw.py:723: RuntimeWarning: Value 100388073 of field POP_EST of feature 161 not successfully written. Possibly due to too larger number with respect to field width
  ogr_write(
/home/6f348dcd-6f88-48fa-ab3c-868ada9b6b8b/.local/lib/python3.10/site-packages/pyogrio/raw.py:723: RuntimeWarning: Value 108116615 of field POP_EST of feature 182 not successfully written. Possibly due to too larger number with respect to field width
  ogr_write(
/home/6f348dcd-6f88-48fa-ab3c-868ada9b6b8b/.local/lib/python3.10/site-packages/pyogrio/raw.py:723: RuntimeWarning: Value 126264931 of field POP_EST of feature 189 not successfully written. Possibly due to too larger number with respect to field width
  ogr_write(
/home/6f348dcd-6f88-48fa-ab3c-868ada9b6b8b/.local/lib/python3.10/site-packages/pyogrio/raw.py:723: RuntimeWarning: Value 'মার্কিন যুক্তরাষ্ট্র পার্শ্ববর্তী ক্ষুদ্র দ্বীপপুঞ্জ' of field NAME_BN has been truncated to 254 characters.  This warning will not be emitted any more for that layer.
  ogr_write(

      Cleaned shapefile saved to: /home/6f348dcd-6f88-48fa-ab3c-868ada9b6b8b/valid_ne_10m_admin_0_countries.shp
      [48]:
      import geopandas as gpd
      import matplotlib.pyplot as plt

      # Load the cleaned shapefile
      cleaned_path = '/home/6f348dcd-6f88-48fa-ab3c-868ada9b6b8b/valid_ne_10m_admin_0_countries.shp'
      cleaned_world = gpd.read_file(cleaned_path)

      # Verify the dataset
      print(cleaned_world.head())
      print(cleaned_world.is_valid.value_counts()) # Ensure no invalid geometries remain

      # Plot the cleaned dataset
      cleaned_world.plot(figsize=(12, 8), color='lightblue', edgecolor='black')
      plt.title('World Map with Cleaned Geometries')
      plt.show()

              featurecla  scalerank  LABELRANK SOVEREIGNT SOV_A3  ADM0_DIF  LEVEL  \
0  Admin-0 country          0          2  Indonesia    IDN         0      2   
1  Admin-0 country          0          3   Malaysia    MYS         0      2   
2  Admin-0 country          0          2      Chile    CHL         0      2   
3  Admin-0 country          0          3    Bolivia    BOL         0      2   
4  Admin-0 country          0          2       Peru    PER         0      2   

                TYPE TLC      ADMIN  ... FCLASS_TR  FCLASS_ID FCLASS_PL  \
0  Sovereign country   1  Indonesia  ...      None       None      None   
1  Sovereign country   1   Malaysia  ...      None       None      None   
2  Sovereign country   1      Chile  ...      None       None      None   
3  Sovereign country   1    Bolivia  ...      None       None      None   
4  Sovereign country   1       Peru  ...      None       None      None   

  FCLASS_GR  FCLASS_IT FCLASS_NL FCLASS_SE  FCLASS_BD FCLASS_UA  \
0      None       None      None      None       None      None   
1      None       None      None      None       None      None   
2      None       None      None      None       None      None   
3      None       None      None      None       None      None   
4      None       None      None      None       None      None   

                                            geometry  
0  MULTIPOLYGON (((117.70361 4.16341, 117.70361 4...  
1  MULTIPOLYGON (((117.70361 4.16341, 117.69711 4...  
2  MULTIPOLYGON (((-69.51009 -17.50659, -69.50611...  
3  POLYGON ((-69.51009 -17.50659, -69.51009 -17.5...  
4  MULTIPOLYGON (((-69.51009 -17.50659, -69.63832...  

[5 rows x 169 columns]
False    249
True       9
Name: count, dtype: int64
      [49]:
      import geopandas as gpd
      from shapely.geometry import Point
      import matplotlib.pyplot as plt

      # Step 2: Load and verify cleaned data
      cleaned_path = '/home/6f348dcd-6f88-48fa-ab3c-868ada9b6b8b/valid_ne_10m_admin_0_countries.shp'
      cleaned_world = gpd.read_file(cleaned_path)

      # Verify the dataset
      print(cleaned_world.head())
      print("Valid geometries count:\n", cleaned_world.is_valid.value_counts()) # Ensure all geometries are valid

      # Step 3: Sample event data
      events = {
      'Event': ['Event A', 'Event B', 'Event C'],
      'Latitude': [40.7128, 34.0522, 41.8781],
      'Longitude': [-74.0060, -118.2437, -87.6298]
      }
      df_events = gpd.GeoDataFrame(
      events,
      geometry=[Point(xy) for xy in zip(events['Longitude'], events['Latitude'])],
      crs="EPSG:4326" # Match CRS of world data
      )

      # Plot the cleaned world map with events overlay
      fig, ax = plt.subplots(figsize=(12, 8))
      cleaned_world.plot(ax=ax, color='lightblue', edgecolor='black')
      df_events.plot(ax=ax, color='red', marker='o', label='Event Locations', alpha=0.8)
      plt.title('World Map with Cleaned Geometries and Event Locations')
      plt.legend()
      plt.show()

              featurecla  scalerank  LABELRANK SOVEREIGNT SOV_A3  ADM0_DIF  LEVEL  \
0  Admin-0 country          0          2  Indonesia    IDN         0      2   
1  Admin-0 country          0          3   Malaysia    MYS         0      2   
2  Admin-0 country          0          2      Chile    CHL         0      2   
3  Admin-0 country          0          3    Bolivia    BOL         0      2   
4  Admin-0 country          0          2       Peru    PER         0      2   

                TYPE TLC      ADMIN  ... FCLASS_TR  FCLASS_ID FCLASS_PL  \
0  Sovereign country   1  Indonesia  ...      None       None      None   
1  Sovereign country   1   Malaysia  ...      None       None      None   
2  Sovereign country   1      Chile  ...      None       None      None   
3  Sovereign country   1    Bolivia  ...      None       None      None   
4  Sovereign country   1       Peru  ...      None       None      None   

  FCLASS_GR  FCLASS_IT FCLASS_NL FCLASS_SE  FCLASS_BD FCLASS_UA  \
0      None       None      None      None       None      None   
1      None       None      None      None       None      None   
2      None       None      None      None       None      None   
3      None       None      None      None       None      None   
4      None       None      None      None       None      None   

                                            geometry  
0  MULTIPOLYGON (((117.70361 4.16341, 117.70361 4...  
1  MULTIPOLYGON (((117.70361 4.16341, 117.69711 4...  
2  MULTIPOLYGON (((-69.51009 -17.50659, -69.50611...  
3  POLYGON ((-69.51009 -17.50659, -69.51009 -17.5...  
4  MULTIPOLYGON (((-69.51009 -17.50659, -69.63832...  

[5 rows x 169 columns]
Valid geometries count:
 False    249
True       9
Name: count, dtype: int64
      [51]:
      # 8. Visual Comparison of Numerical and Categorical Variables by Region
      import plotly.express as px
      import pandas as pd

      # Sample dataset with numerical and categorical variables (Population and Region)
      data = {
      'Country': ['USA', 'China', 'India', 'Brazil', 'Germany', 'UK', 'France', 'Italy', 'Spain', 'Australia'],
      'Population': [331002651, 1439323776, 1380004385, 212559417, 83783942, 67886011, 65273511, 60244639, 46719142, 25499884],
      'Region': ['North America', 'Asia', 'Asia', 'South America', 'Europe', 'Europe', 'Europe', 'Europe', 'Europe', 'Oceania']
      }

      # Convert to DataFrame
      df = pd.DataFrame(data)

      # Visualizing Population by Region on a world map
      fig = px.choropleth(df,
      locations="Country",
      locationmode="country names", # Specify countries
      color="Population", # Numerical variable
      hover_name="Country", # Show country name on hover
      color_continuous_scale="Viridis", # Color scale
      animation_frame="Region", # Animate by region
      title="World Map: Population by Region"
      )

      # Show the plot
      fig.show()

      Region=North AmericaNorth AmericaAsiaSouth AmericaEuropeOceania331.0026506M331.0026508M331.002651M331.0026512M331.0026514MPopulationWorld Map: Population by Region▶◼
      plotly-logomark
      [53]:
      # Day 3: Visualizing Network Data
      # Task 9: Network Graph Visualization
      # Objective:Create a network graph using `networkx
      import networkx as nx
      import matplotlib.pyplot as plt

      # Create a simple graph
      G = nx.Graph()

      # Add nodes and edges
      G.add_edges_from([(1, 2), (2, 3), (3, 4), (4, 1)])

      # Draw the graph
      nx.draw(G, with_labels=True)
      plt.show()


      [56]:
      # Task 9b: Visualizing a Network of Friendships
      import networkx as nx
      import matplotlib.pyplot as plt

      # Create a graph object
      G = nx.Graph()

      # Add nodes (people)
      G.add_nodes_from(['Alice', 'Bob', 'Charlie', 'David', 'Eve'])

      # Add edges (friendships) between people
      G.add_edges_from([('Alice', 'Bob'),
      ('Alice', 'Charlie'),
      ('Bob', 'David'),
      ('Charlie', 'David'),
      ('Eve', 'Charlie')])

      # Draw the network
      plt.figure(figsize=(8, 6))
      nx.draw(G, with_labels=True, node_color='skyblue', node_size=2000, font_size=14, font_weight='bold', edge_color='gray')

      # Display the plot
      plt.title("Social Network of Friendships")
      plt.show()

      [54]:
      # Task 10: Visualizing Central Nodes in a Social Network
      # Objective:Identify and highlight central nodes (influencers) in a network
      # Compute degree centrality
      centrality = nx.degree_centrality(G)

      # Draw the graph with nodes sized by centrality
      node_sizes = [centrality[node] * 1000 for node in G.nodes]
      nx.draw(G, with_labels=True, node_size=node_sizes, node_color='skyblue')
      plt.show()


      [58]:
      # Task 10b: Visualizing Central Nodes (Influencers)
      # Objective: The goal is to identify and highlight central nodes (influencers) in the network
      import networkx as nx
      import matplotlib.pyplot as plt

      # Create a graph object
      G = nx.Graph()

      # Add nodes and edges (friendships or interactions)
      G.add_edges_from([('Alice', 'Bob'),
      ('Alice', 'Charlie'),
      ('Bob', 'David'),
      ('Charlie', 'David'),
      ('Eve', 'Charlie'),
      ('Charlie', 'Frank')])

      # Calculate degree centrality
      centrality = nx.degree_centrality(G)

      # Get the node with the highest centrality
      most_central_node = max(centrality, key=centrality.get)

      # Draw the network
      plt.figure(figsize=(7, 6))
      nx.draw(G, with_labels=True, node_size=2000, node_color='skyblue', font_size=14, font_weight='bold', edge_color='gray')

      # Highlight the most central node
      plt.title(f"Most Central Node: {most_central_node}")
      plt.show()

      # Print centrality values
      print("Degree Centrality of Nodes:")
      for node, value in centrality.items():
      print(f"{node}: {value:.2f}")


      Degree Centrality of Nodes:
Alice: 0.40
Bob: 0.40
Charlie: 0.80
David: 0.40
Eve: 0.20
Frank: 0.20
      [10]:
      # To help resolve the issue with `community` not being found, let's identify the current environment and verify the
      # presence of the `community` package. Let's check the installed packages and see if `community` (or python-louvain) is listed.

      !pip list | grep "community\|python-louvain"

      [11]:
      import community as community_louvain
      print("python-louvain module loaded successfully")

      ---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
Cell In[11], line 1
----> 1 import community as community_louvain
      2 print("python-louvain module loaded successfully")

ModuleNotFoundError: No module named 'community'
      [8]:
      # Task 11: Detecting Communities in a Social Network**
      # Objective: To identify communities or clusters within a social network
      import networkx as nx
      import matplotlib.pyplot as plt
      import community as community_louvain

      # Create a graph object
      G = nx.Graph()

      # Add nodes and edges (friendships or interactions)
      G.add_edges_from([('Alice', 'Bob'),
      ('Alice', 'Charlie'),
      ('Bob', 'David'),
      ('Charlie', 'David'),
      ('Eve', 'Charlie'),
      ('Charlie', 'Frank')])

      # Use the Louvain method for community detection
      partition = community_louvain.best_partition(G)

      # Draw the network with communities colored
      plt.figure(figsize=(8, 8))

      # Get community colors
      colors = [partition[node] for node in G.nodes()]

      # Draw nodes and edges with colors based on communities
      nx.draw(G, with_labels=True, node_size=2000, node_color=colors, cmap=plt.cm.rainbow, font_size=14, font_weight='bold', edge_color='gray')

      plt.title("Community Detection in Network")
      plt.show()

      # Print the detected communities
      print("Detected Communities:")
      for node, comm in partition.items():
      print(f"{node}: Community {comm}")


      ---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
Cell In[8], line 5
      3 import networkx as nx
      4 import matplotlib.pyplot as plt
----> 5 import community as community_louvain
      7 # Create a graph object
      8 G = nx.Graph()

ModuleNotFoundError: No module named 'community'
      [3]:
      # Task 12: Significance of Edge Thickness and Node Size in Network Visualizations
      # Objective: Explore how adjusting edge thickness and node size can help emphasize different aspects of a network
      import networkx as nx
      import matplotlib.pyplot as plt

      # Create a graph object
      G = nx.Graph()

      # Add nodes and edges with different weights (edge weights represent strength of connection)
      G.add_edge('Alice', 'Bob', weight=2)
      G.add_edge('Alice', 'Charlie', weight=4)
      G.add_edge('Bob', 'David', weight=1)
      G.add_edge('Charlie', 'David', weight=3)
      G.add_edge('Eve', 'Charlie', weight=2)
      G.add_edge('Charlie', 'Frank', weight=5)

      # Get edge weights for visualization
      edge_weights = [G[u][v]['weight'] for u, v in G.edges()]

      # Draw the network with varying edge thickness based on weights
      plt.figure(figsize=(7, 6))

      # Adjust node size based on degree centrality
      node_size = [1000 * G.degree(node) for node in G.nodes()]

      # Draw the network with edge width based on weight and node size based on degree
      nx.draw(G, with_labels=True, node_size=node_size, font_size=14, font_weight='bold', edge_color='gray', width=edge_weights, node_color='skyblue')

      plt.title("Network with Edge Thickness and Node Size")
      plt.show()


      [7]:
      # Task 13: Visualizing Hierarchical Data (Tree/Sunburst Chart)
      # Objective:Visualize hierarchical data using `plotly`
      import pandas as pd
      import plotly.express as px

      # Sample hierarchical data (product categories and subcategories)
      data = {
      'Parent': ['Root', 'Root', 'Electronics', 'Electronics', 'Furniture', 'Furniture'],
      'Child': ['Electronics', 'Furniture', 'Mobile', 'Laptop', 'Chair', 'Table'],
      'Value': [10, 15, 5, 5, 8, 7]
      }

      df_hierarchy = pd.DataFrame(data)

      # Plot sunburst chart
      fig = px.sunburst(df_hierarchy, path=['Parent', 'Child'], values='Value')
      fig.show()
      [3]:
      # Day 4: Visualizing Hierarchical Data
      # Task 14: Visualizing the Hierarchical Relationship Between Products in a Retail Business
      import plotly.express as px
      import pandas as pd

      # Sample dataset representing a product hierarchy
      data = {
      "Category": ["Electronics", "Electronics", "Electronics", "Clothing", "Clothing", "Clothing", "Furniture"],
      "Subcategory": ["Phones", "Laptops", "Tablets", "Men", "Women", "Kids", "Chairs"],
      "Product": ["iPhone", "MacBook", "iPad", "Shirts", "Dresses", "Uniforms", "Office Chair"],
      "Sales": [12000, 18000, 15000, 9000, 8000, 5000, 10000]
      }

      # Create a DataFrame
      df = pd.DataFrame(data)

      # Create a sunburst chart
      fig = px.sunburst(df, path=['Category', 'Subcategory', 'Product'], values='Sales',
      title="Product Hierarchy in Retail Business")
      fig.update_layout(margin=dict(t=0, l=0, r=0, b=0))
      fig.show()

      [6]:
      # Day 4: Visualizing Hierarchical Data
      # Task 15: Visualizing a Decision Tree Hierarchy
      # Example Code for Decision Tree Visualization
      from sklearn.tree import DecisionTreeClassifier, plot_tree
      import matplotlib.pyplot as plt
      from sklearn.datasets import load_iris
      from sklearn.model_selection import train_test_split

      # Load sample data (using Iris dataset as a placeholder for product/customer decision data)
      data = load_iris()
      X = data.data
      y = data.target

      # Train a decision tree classifier
      clf = DecisionTreeClassifier(max_depth=3, random_state=0)
      clf.fit(X, y)

      # Plot the decision tree
      plt.figure(figsize=(12, 8))
      plot_tree(clf, feature_names=data.feature_names, class_names=list(data.target_names), filled=True, rounded=True)
      plt.title("Decision Tree for Customer Decision Making")
      plt.show()

      [ ]:

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